@InProceedings{AlvesFerrLima:2018:MéHíFu,
author = "Alves, Emilly Pereira and Ferreira, Felipe Alberto Barbosa
Sim{\~a}o and Lima, M{\'a}rcio Jos{\'e} de Carvalho",
affiliation = "{Universidade de Pernambuco} and {Universidade Federal de
Pernambuco} and {Universidade de Pernambuco}",
title = "Um m{\'e}todo h{\'{\i}}brido fuzzy-swarm-clustering para
segmenta{\c{c}}{\~a}o de MRI",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "Sociedade Brasileira de Computa{\c{c}}{\~a}o",
address = "Porto Alegre",
keywords = "segmenta{\c{c}}{\~a}o de imagens, l{\'o}gica fuzzy,
intelig{\^e}ncia de enxames, MRI.",
abstract = "The segmentation process in Magnetic Resonance Imaging (MRI)
stands out when it acts in the detection of different regions of
the brain. Among the used techniques, clustering segmentation
methods have been commonly used in the literature. In order to
optimize the already existing techniques, this paper proposes a
hybrid technique with Fuzzy C-Means and Particle Swarm
Optimization algorithms. With the purpose of evaluating the
algorithms performance, synthetic images and brain simulated MRI
were used. The performance was measured in terms of Peak
Signal-to-noise Ratio (PSNR), Segmentation Accuracy (SA) and Mean
Squared Error (MSE).",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
language = "pt",
ibi = "8JMKD3MGPAW/3S36EU2",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3S36EU2",
targetfile = "wuw_paper_20_camera_ready.pdf",
urlaccessdate = "2024, May 04"
}